Evans, Marissa Ann
(2023)
Dynamic changes in sleep characteristics and pre-sleep arousal during cognitive behavioral therapy for insomnia as predictors of treatment response.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Insomnia is a highly prevalent and distressing sleep disorder that is strongly associated with poor health among older adults. Cognitive-behavioral therapy for insomnia (CBT-I) is an evidence-based treatment for insomnia. Clinicians use patients’ daily sleep diaries to monitor and modify treatment during each of eight sessions. Prior research on CBT-I, however, attempts to draw conclusions about the course of CBT-I by analyzing changes from pre-intervention to post-intervention only. The objective of this study was to more closely match clinical care by assessing dynamic changes in sleep characteristics and pre-sleep arousal during the course of CBT-I to predict treatment response.
Ninety-four older adults (Mage = 68) completed daily sleep diary data for twelve weeks (Mdays= 70) before, during, and after CBT-I. Diary data were used to assess pre-sleep arousal, sleep latency, efficiency, quality, duration, wake after sleep onset, and alertness. We assessed dynamic changes in sleep and pre-sleep arousal by latent growth curve modeling, as well as their bidirectional association by random-intercept cross-lagged panel modeling. We used these models to predict treatment outcome, defined as the change in the Insomnia Severity Index score from pre- to post-intervention.
All models of change were best characterized by non-linear quadratic terms. We observed substantial improvements in wake after sleep onset, sleep efficiency, sleep duration, and sleep quality. We also observed statistically significant, yet less substantial, changes in pre-sleep arousal and alertness. In models of bidirectional associations, higher levels of alertness assessed at one timepoint predicted subsequent lower levels of pre-sleep arousal at the next timepoint when adjusted for previous levels of both variables. Greater improvements in sleep efficiency and sleep quality predicted better treatment outcomes, whereas sharper initial declines in alertness portended poorer treatment outcomes. Pre-intervention data did not predict treatment outcome.
This study was the first to our knowledge to apply advanced statistical methods to model the dynamics of sleep and pre-sleep arousal data collected throughout the course of CBT-I. Our results demonstrate that documenting and observing changes during therapy has added benefit for effective patient care over contemporary pre-post intervention analyses.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
6 September 2023 |
| Date Type: |
Publication |
| Defense Date: |
20 April 2022 |
| Approval Date: |
6 September 2023 |
| Submission Date: |
27 April 2022 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
90 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Dietrich School of Arts and Sciences > Psychology |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
insomnia, cognitive behavioral therapy, older adults, latent growth curve model, clinical psychology, health psychology |
| Date Deposited: |
06 Sep 2023 15:12 |
| Last Modified: |
06 Sep 2023 15:12 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/42763 |
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